Publications

The linear sequence of the genome has been extremely valuable in mapping regulatory elements relative to the genes they control. However, it has become increasingly evident that characterizing the three-dimensional organization of the genome is critical to get a better understanding of long-range regulation. Early studies using fluorescent in-situ hybridization (FISH) revealed that individual chromosomes occupy distinct spaces in the nucleus with minimal intermingling between territories[1]. Recent advances using chromosome conformation capture (3C) techniques have confirmed these findings and further improved the depth at which we can determine the organization of chromosomes and the physical interactions that occur within and between them[2, 3]. Variations of the 3C technique include (i) Hi-C, to capture all pairwise interactions, (ii) 5C, to capture interactions within and between loci of interest and (iii) 4C-Seq, to capture all interactions with a single locus of interest. The choice of technique depends on the biological question being asked and the scale at which this needs to be examined. While Hi-C has been instrumental in characterizing higher-order organization of chromosomes in the nucleus, it lacks the resolution that is required for analysis of specific interactions, such as between enhancers and promoters. This can be achieved with 4C-Seq, which allows interrogation of interactions from a single viewpoint or bait, to the rest of the genome. Several studies have used 4C-Seq to better understand phenomena such as X chromosome inactivation[4], enhancer-promoter interactions[5, 6], organization of antigen receptor loci[7], choice of translocation partners[8, 9] and collinear transcriptional regulation[10]. Here we aim to focus on the current state of the 4C-Seq method and the limitations and challenges of the associated computational analysis.

The present invention relates to oligooxopiperzines for modulating the p53-Mdm2 interaction. Methods of using the oligooxopiperazines are also disclosed.

MOTIVATION:
Leveraging the large compendium of genomic data to predict biomedical pathways and specific mechanisms of protein interactions genome-wide in metazoan organisms has been challenging. In contrast to unicellular organisms, biological and technical variation originating from diverse tissues and cell-lineages is often the largest source of variation in metazoan data compendia. Therefore, a new computational strategy accounting for the tissue heterogeneity in the functional genomic data is needed to accurately translate the vast amount of human genomic data into specific interaction-level hypotheses.

RESULTS:
We developed an integrated, scalable strategy for inferring multiple human gene interaction types that takes advantage of data from diverse tissue and cell-lineage origins. Our approach specifically predicts both the presence of a functional association and also the most likely interaction type among human genes or its protein products on a whole-genome scale. We demonstrate that directly incorporating tissue contextual information improves the accuracy of our predictions, and further, that such genome-wide results can be used to significantly refine regulatory interactions from primary experimental datasets (e.g. ChIP-Seq, mass spectrometry).

AVAILABILITY AND IMPLEMENTATION:
An interactive website hosting all of our interaction predictions is publically available at http://pathwaynet.princeton.edu. Software was implemented using the open-source Sleipnir library, which is available for download at https://bitbucket.org/libsleipnir/libsleipnir.bitbucket.org.

Lymphocytic infiltration is associated with better prognosis in several epithelial malignancies including breast cancer. The tumor suppressor TP53 is mutated in approximately 30% of breast adenocarcinomas, with varying frequency across molecular subtypes. In this study of 1,420 breast tumors, we tested for interaction between TP53 mutation status and tumor subtype determined by PAM50 and integrative cluster analysis. In integrative cluster 10 (IC10)/basal-like breast cancer, we identify an association between lymphocytic infiltration, determined by an expression score, and retention of wild-type TP53. The expression-derived score agreed with the degree of lymphocytic infiltration assessed by pathologic review, and application of the Nanodissect algorithm was suggestive of this infiltration being primarily of cytotoxic T lymphocytes (CTL). Elevated expression of this CTL signature was associated with longer survival in IC10/Basal-like tumors. These findings identify a new link between the TP53 pathway and the adaptive immune response in estrogen receptor (ER)-negative breast tumors, suggesting a connection between TP53 inactivation and failure of tumor immunosurveillance.

Experiments in animal models are often conducted to infer how humans will respond to stimuli by assuming that the same biological pathways will be affected in both organisms. The limitations of this assumption were tested in the IMPROVER Species Translation Challenge, where 52 stimuli were applied to both human and rat cells and perturbed pathways were identified. In the Inter-species Pathway Perturbation Prediction sub-challenge, multiple teams proposed methods to use rat transcription data from 26 stimuli to predict human gene set and pathway activity under the same perturbations. Submissions were evaluated using three performance metrics on data from the remaining 26 stimuli.

We present SEEK (search-based exploration of expression compendia; http://seek.princeton.edu/), a query-based search engine for very large transcriptomic data collections, including thousands of human data sets from many different microarray and high-throughput sequencing platforms. SEEK uses a query-level cross-validation–based algorithm to automatically prioritize data sets relevant to the query and a robust search approach to identify genes, pathways and processes co-regulated with the query. SEEK provides multigene query searching with iterative metadata-based search refinement and extensive visualization-based analysis options.

Scholars and commentators have debated whether lower-threshold forms of political engagement on social media should be treated as being conducive to higher-threshold modes of political participation or a diversion from them. Drawing on an original survey of a representative sample of Italians who discussed the 2013 election on Twitter, we demonstrate that the more respondents acquire political information via social media and express themselves politically on these platforms, the more they are likely to contact politicians via e-mail, campaign for parties and candidates using social media, and attend offline events to which they were invited online. These results suggest that lower-threshold forms of political engagement on social media do not distract from higher-threshold activities, but are strongly associated with them.

...These events — and the corresponding responses on social media — illustrate what has become increasingly evident: it is almost impossible to think of a major political protest or upheaval occurring without social media being part of both the incident and the ensuing narrative. The Euromaidan protests, which culminated in the flight of President Yanukovych from Ukraine in late February 2014, are a case in point. Indeed, the Ukrainian Euromaidan protest movement may go down in history as the first truly successful social media uprising. Earlier movements labeled social media revolutions subsequently have been criticized for not having had much important activity on social media (Moldova, Arab Spring) or for having had a large social media presence but ultimately failing to make much of a long-term impact as a protest movement (Spain’s Los Indignados, Occupy Wall Street, Gezi Park in Turkey). In Ukraine, a government fell, a region was annexed, a civilian plane was shot down, and what some are calling a civil war continues to this day in the eastern part of the country. Clearly, the movement was consequential and, as we will show, social media usage was widespread and significant.

Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computational biology have led to multiple large regulatory network models (directed networks) each with a large corpus of supporting data and gene-annotation. There are multiple possible biological motivations for exploring large regulatory network models, including: validating TF-target gene relationships, figuring out co-regulation patterns, and exploring the coordination of cell processes in response to changes in cell state or environment. Here we focus on queries aimed at validating regulatory network models, and on coordinating visualization of primary data and directed weighted gene regulatory networks. The large size of both the network models and the primary data can make such coordinated queries cumbersome with existing tools and, in particular, inhibits the sharing of results between collaborators. In this work, we develop and demonstrate a web-based framework for coordinating visualization and exploration of expression data (RNA-seq, microarray), network models and gene-binding data (ChIP-seq). Using specialized data structures and multiple coordinated views, we design an efficient querying model to support interactive analysis of the data. Finally, we show the effectiveness of our framework through case studies for the mouse immune system (a dataset focused on a subset of key cellular functions) and a model bacteria (a small genome with high data-completeness).

Social media use among elites offers a useful avenue for analyzing regime response to protest, especially in countries with some degree of freedom of speech. We examine the frequency and content of Twitter usage among Venezuelan elites in the context of the 2014 protests. This analysis demonstrates that the regime sent more signals during protests but that the content of these messages addressed more topics than it did for opposition elites, especially following acts of regime suppression of opposition-sponsored protests. This observation supports theoretical predictions that noisy public information can make coordination more difficult. Regime elites produced noisy Twitter as part of an explicit strategy to prevent citizens from coordinating on revolution. This analysis of social media use by both pro- and anti-regime elites contributes to the debate over whether social media prevents or promotes regime change.